Quantum Dots for Cancer Imaging

  • Swadeshmukul Santra
  • Debamitra Dutta
Part of the Fundamental Biomedical Technologies book series (FBMT, volume 102)


During the last century cancer has slowly advanced to become the leading cause of death for patients below the age of 85 in USA in spite of rapid advances in global cancer research toward the understanding of cancer biology in the past several decades. Early cancer diagnosis, in combination with the precise cancer therapies, could eventually save millions of lives. The diagnosis of cancer at the early stage is therefore extremely important and has been an active research area of great interest in current times.

Nanoparticle technology is an interdisciplinary research area of nanoscale science and technology. An emerging branch of nanoparticle technology is the development of optical-based functional nanoparticles, such as luminescent quantum dots (Qdots), having strong potential to revolutionize nanomedicine research, specifically in the area of optical contrast agent development and their applications in various bioimaging techniques including cancer imaging.

This chapter provides a knowledge-based platform to readers who are interested in learning more about the potential of Qdot nanotechnology for diagnostic cancer imaging (Section 22.1). Our goal is to provide readers (i) the basics of luminescent Qdots, (ii) details of Qdot design, synthesis, and bioconjugate chemistries to target cancers (Section 22.2), and (iii) applications of Qdot technology in cancer imaging (Section 22.3) that include a brief description of the benefits, challenges, limitations, and the future scope of Qdot technology in both in vitro and in vivo cancer imaging applications. Finally, Section 22.4 provides concluding remarks and the perspectives of Qdot-based optical imaging of cancers.


Fluorescence Resonance Energy Transfer Cancer Imaging Sentinel Lymph Node Mapping CdSe Nanocrystals Bioconjugate Chemistry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Swadeshmukul Santra
  • Debamitra Dutta

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